Read the summary and the most important questions on Cognitive Control & Decision Making

• 1 Lecture 2 - comparative neuroanatomy of prefrontal cortex

• 1.2 Deel 2

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• Medial prefrontal cortex. Properties:

  1. Few sensory inputs compared to other PFC areas
  2. Connections to medial premotor regions involved in controlling actions based on internal cues
  3. Connections to hippocampus and amygdala suggesting access to memories of past events and information about outcomes valued in terms of current biological needs.

• Orbital prefrontal cortex. Properties

  • Connections with olfactory, gustatory, and visual cortex -> allowing a representation of sensory outcomes
  • Extensive interconnections with medial PFC
  • Extensive amygdala connections
  • Strong inferior temporal and perirhinal connections providing information about objects

• 1.3 Deel 3

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• Ventral prefrontal cortex. Properties

  1. Strong connections to higher-order temporal areas representing conjunctions of stimulus features. This contrast with the lower-level connections of caudal PFC.
  2. Multi-model sensory information
  3. Inferior parietal connections involved in objects and grasping
  4. Connections to premotor areas involved in hand and mouth actions
  5. Information about current needs directly from amygdala or via orbital PFC

• 2 Lecture 3 - Cognitive Hierarchies in medial and lateral PFC

• 2.1.2 Early models of Cognitive Control

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• Summary of early models of cognitive control

  ACC as an action selector:
  

  • Conflict monitoring model
  • Principle: mPFC/ACC tracks conflict
  • Error likelihood model
  • Principle: mPFC/ACC tracks error likelihood

  
  ACC as an evaluative controller:
  

  • Motor control filter model
  • Principle: mPFC/ACC selects policies (sequences of actions towards a goal) depending on a value.

• 2.2.1 Recent models - Effort

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• Choice difficulty (Botvinick 2007, Shenhav et al. 2014)

  • ACC codes for choice difficulty (i.e. Conflict between choice options).
  • ACC activity during decision-making increases when 2 options are close in value (i.e. It's more difficult to choose) and decreases when the value difference is large (one is a lot better than the other one).
  • Example: choose between chocolate cake or apple pie, assuming you like both -> difficult choice  

• Choice difficulty, Adaptive effort allocation & expected value of control are:

  1. specific to the mPFC/ACC in cognitive control/effort exertion
  2. based on fMRI and/or EEG data

• 2.2.2 Recent models - Integrative

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• PRO, RVPM and HRL-ACC account for

  • mPFC/ACC activity across tasks and across modalities
  • Based on fMRI data, EEG data, animal work and what is recorded in neurons in animal studies and also across tasks 

• 3 Lecture 4 - The interplay between decisions and percepts

• 3.3 Deel 3

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• Computation of Action selection

  choose actions based on valuations
  
  

• 4 Lecture 5 - Neuroeconomics Prospect Theory

• 4.3 Deel 3

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• In addition to whatever you own, you have been given 1000. You are now asked to choose between 1000_0.5  /  0 and 500.-> people choose 500In addition to whatever you own you have been given 2000. You are now asked to choose between -1000_0.5   /  0 and -500-> people choose   -1000_0.5   /  0.why?

  Already in advance knowing that you certainty lose something, is something you hate. In the other option there is still a chance to lose nothing. That is making you more risk seeking in the negative domain.

• Why is the function steeper in the negative domain?

  This is the loss domain 
  Pain of losses is felt λ  as much as the joy of gains